题目
Problem
f ( x ) = x 2 − 7 x − 4 x x 3 x > 0 f(x) = x^2 - \frac{7x - 4\sqrt{x}}{x^3}\quad x > 0 f ( x ) = x 2 − x 3 7 x − 4 x x > 0
(a) Show that the equation f ( x ) = 0 f(x) = 0 f ( x ) = 0 has a root α \alpha α in the interval [ 0.3 , 0.4 ] [0.3, 0.4] [ 0.3 , 0.4 ]
(2)
(b) Determine f ′ ( x ) f'(x) f ′ ( x ) .
(3)
(c) Using x 0 = 0.3 x_0 = 0.3 x 0 = 0.3 as a first approximation for α \alpha α , apply the Newton–Raphson procedure once to f ( x ) f(x) f ( x ) to determine a second approximation for α \alpha α , giving your answer to 3 decimal places.
(2)
The equation f ( x ) = 0 f(x) = 0 f ( x ) = 0 has another root β \beta β in the interval [ 1.3 , 1.5 ] [1.3, 1.5] [ 1.3 , 1.5 ]
(d) Use linear interpolation once on the interval [ 1.3 , 1.5 ] [1.3, 1.5] [ 1.3 , 1.5 ] to determine an approximation for β \beta β , giving your answer to 3 decimal places.
(2)
题目中文翻译
已知函数
f ( x ) = x 2 − 7 x − 4 x x 3 x > 0 f(x) = x^2 - \frac{7x - 4\sqrt{x}}{x^3}\quad x > 0 f ( x ) = x 2 − x 3 7 x − 4 x x > 0
(a) 证明方程 f ( x ) = 0 f(x) = 0 f ( x ) = 0 在区间 [ 0.3 , 0.4 ] [0.3, 0.4] [ 0.3 , 0.4 ] 内有一个根 α \alpha α 。
(b) 求出 f ′ ( x ) f'(x) f ′ ( x ) 。
(c) 以 x 0 = 0.3 x_0 = 0.3 x 0 = 0.3 作为 α \alpha α 的首次近似值,对 f ( x ) f(x) f ( x ) 应用一次 Newton–Raphson 迭代法以确定 α \alpha α 的第二次近似值,结果保留 3 位小数。
方程 f ( x ) = 0 f(x) = 0 f ( x ) = 0 在区间 [ 1.3 , 1.5 ] [1.3, 1.5] [ 1.3 , 1.5 ] 内有另一个根 β \beta β 。
(d) 在区间 [ 1.3 , 1.5 ] [1.3, 1.5] [ 1.3 , 1.5 ] 上应用一次线性插值法(linear interpolation)来确定 β \beta β 的近似值,结果保留 3 位小数。
解答
(a)
解法一
思路
展开
分别计算区间两个端点处的函数值。由于 f f f 在 x > 0 x>0 x > 0 上连续,只要两个函数值异号,便可由介值定理判断区间内存在一个根。
答题过程
展开
At the endpoints of the interval,
f ( 0.3 ) = 3.456304 … > 0 f(0.3)=3.456304\ldots>0 f ( 0.3 ) = 3.456304 … > 0
and
f ( 0.4 ) = − 4.061529 … < 0. f(0.4)=-4.061529\ldots<0. f ( 0.4 ) = − 4.061529 … < 0.
Since f f f is continuous for x > 0 x>0 x > 0 and there is a change of sign, the equation f ( x ) = 0 f(x)=0 f ( x ) = 0 has a root α \alpha α in the interval
[ 0.3 , 0.4 ] . \boxed{[0.3,0.4]}. [ 0.3 , 0.4 ] .
(b)
解法一
思路
展开
先把分式改写为幂函数之和,再逐项求导。这样可以直接使用幂函数求导法则,减少使用商法则时的代数运算。
答题过程
展开
First rewrite f ( x ) f(x) f ( x ) using indices:
f ( x ) = x 2 − 7 x − 4 x x 3 = x 2 − 7 x − 2 + 4 x − 5 2 . \begin{align*}
f(x)
=&\,x^2-\frac{7x-4\sqrt{x}}{x^3}\\
=&\,x^2-7x^{-2}+4x^{-\frac52}.
\end{align*} f ( x ) = = x 2 − x 3 7 x − 4 x x 2 − 7 x − 2 + 4 x − 2 5 .
Therefore,
f ′ ( x ) = 2 x + 14 x − 3 − 10 x − 7 2 . \begin{align*}
f'(x)
=&\,2x+14x^{-3}-10x^{-\frac72}.
\end{align*} f ′ ( x ) = 2 x + 14 x − 3 − 10 x − 2 7 .
Hence,
f ′ ( x ) = 2 x + 14 x − 3 − 10 x − 7 2 . \boxed{f'(x)=2x+14x^{-3}-10x^{-\frac72}}. f ′ ( x ) = 2 x + 14 x − 3 − 10 x − 2 7 .
(c)
解法一
思路
展开
将首次近似值 x 0 = 0.3 x_0=0.3 x 0 = 0.3 代入 Newton–Raphson 公式 x n + 1 = x n − f ( x n ) f ′ ( x n ) x_{n+1}=x_n-\frac{f(x_n)}{f'(x_n)} x n + 1 = x n − f ′ ( x n ) f ( x n ) ,只迭代一次并将结果保留 3 位小数。
答题过程
展开
Using the Newton–Raphson formula
x n + 1 = x n − f ( x n ) f ′ ( x n ) , x_{n+1}=x_n-\frac{f(x_n)}{f'(x_n)}, x n + 1 = x n − f ′ ( x n ) f ( x n ) ,
with x 0 = 0.3 x_0=0.3 x 0 = 0.3 , we have
f ( 0.3 ) = 3.456304815 … f(0.3)=3.456304815\ldots f ( 0.3 ) = 3.456304815 …
and
f ′ ( 0.3 ) = − 157.0821698 … . f'(0.3)=-157.0821698\ldots. f ′ ( 0.3 ) = − 157.0821698 … .
Thus
x 1 = 0.3 − 3.456304815 … − 157.0821698 … = 0.322003164 … . \begin{align*}
x_1
=&\,0.3-\frac{3.456304815\ldots}{-157.0821698\ldots}\\
=&\,0.322003164\ldots.
\end{align*} x 1 = = 0.3 − − 157.0821698 … 3.456304815 … 0.322003164 … .
Therefore, to 3 decimal places,
α ≈ 0.322 . \boxed{\alpha\approx0.322}. α ≈ 0.322 .
(d)
解法一
思路
展开
先求 f ( 1.3 ) f(1.3) f ( 1.3 ) 与 f ( 1.5 ) f(1.5) f ( 1.5 ) ,再用连接两端点的直线与 x x x 轴的交点作为根的线性插值近似值。
答题过程
展开
At the endpoints,
f ( 1.3 ) = − 0.3761348655 … f(1.3)=-0.3761348655\ldots f ( 1.3 ) = − 0.3761348655 …
and
f ( 1.5 ) = 0.5904383661 … . f(1.5)=0.5904383661\ldots. f ( 1.5 ) = 0.5904383661 … .
Using linear interpolation,
β = 1.3 f ( 1.5 ) − 1.5 f ( 1.3 ) f ( 1.5 ) − f ( 1.3 ) = 1.3 ( 0.5904383661 … ) − 1.5 ( − 0.3761348655 … ) 0.5904383661 … − ( − 0.3761348655 … ) = 1.377828529 … . \begin{align*}
\beta
=&\,\frac{1.3f(1.5)-1.5f(1.3)}{f(1.5)-f(1.3)}\\
=&\,\frac{1.3(0.5904383661\ldots)-1.5(-0.3761348655\ldots)}
{0.5904383661\ldots-(-0.3761348655\ldots)}\\
=&\,1.377828529\ldots.
\end{align*} β = = = f ( 1.5 ) − f ( 1.3 ) 1.3 f ( 1.5 ) − 1.5 f ( 1.3 ) 0.5904383661 … − ( − 0.3761348655 … ) 1.3 ( 0.5904383661 … ) − 1.5 ( − 0.3761348655 … ) 1.377828529 … .
Therefore, to 3 decimal places,
β ≈ 1.378 . \boxed{\beta\approx1.378}. β ≈ 1.378 .